Evaluation of Three Vision-Based Object Perception Methods for a Mobile Robotic Platform
Publication Type:
Journal ArticleSource:
Journal of Intelligent and Robotic Systems , Volume 68, Issue 2, p.185-208 (2012)Abstract:
This paper addresses object perception applied to mobile robotics. Being able to perceive semantically meaningful objects in unstructured environments is a ke capability in order to make robots suitable to perform high-level tasks in home environments. However, finding a solution for this task is daunting: it requires the ability to handle the variability in image formation in a moving camera with tight time constraints. The paper brings to attention some of the issues with applying three state of the art object recognition and detection methods in a mobile robotics scenario, and
proposes methods to deal with windowing/segmentation. Thus, this work aims at evaluating the state-of-the-art in object perception in an attempt to develop a lightweight solution for mobile robotics use/research in typical indoor settings.
Object Segmentation using a Bag of Features Approach
Fast and Robust Object Segmentation with the Integral Linear Classifier
Robust Vision-Based Localization using Combinations of Local Feature Regions Detectors
Publication Type:
Journal ArticleSource:
Autonomous Robots Journal, Volume 27, Issue 4, p.373-385 (2009)Abstract:
This paper presents a vision-based approach
for mobile robot localization. The model of the environment
is topological. The new approach characterize a
place using a signature. This signature consists of a constellation
of descriptors computed over di erent types
of local affine covariant regions extracted from an omnidirectional
image acquired rotating a standard camera
with a pan-tilt unit. This type of representation permits
a reliable and distinctive environment modeling.
Our objectives were to validate the proposed method
in indoor environments and, also, to nd out if the
combination of complementary local feature region detectors
improves the localization versus using a single
region detector. Our experimental results show that if
false matches are efectively rejected, the combination
of di erent covariant affine region detectors increases
notably the performance of the approach by combining
the di erent strengths of the individual detectors.
In order to reduce the localization time, two strategies
are evaluated
Eficient Object Pixel-Level Categorization using Bag of Features
Publication Type:
Conference PaperSource:
5th International Symposium on Visual Computing, Springer, Volume 5875, p.44-54 (2009)Abstract:
In this paper we present a pixel-level object categorization
method suitable to be applied under real-time constraints. Since pixels
are categorized using a bag of features scheme, the major bottleneck of
such an approach would be the feature pooling in local histograms of
visual words. Therefore, we propose to bypass this time-consuming step
and directly obtain the score of a linear Support Vector Machine classi-
er. This is achieved by creating an integral image of the components of
the SVM which can readily obtain the classi cation score for any image
sub-window with only 10 additions and 2 products, regardless of its size.
Besides, we evaluated the performance of two ecient feature quantiza-
tion methods: the Hierarchical K-Means and the Extremely Randomized
Forest. All experiments have been done in the Graz02 database, showing
comparable, or even better results to related work with a lower compu-
tational cost.
Visual Registration Method for a Low Cost Robot
Publication Type:
Conference PaperSource:
7th International Conference on Computer Vision Systems. Lecture Notes in Computer Science, Springer, Volume 5815, Liege, Belgium, p.204-214 (2009)ISBN:
3-642-04666-5Keywords:
Registration; Bag of features; robot localizationAbstract:
An autonomous mobile robot must face the correspondence
or data association problem in order to carry out
tasks like place recognition or unknown environment mapping. In
order to put into correspondence two maps, most correspondence
methods first extract early features from robot sensor data,
then matches between features are searched and finally the
transformation that relates the maps is estimated from such
matches. However, finding explicit matches between features is a
challenging and computationally expensive task. In this paper, we
propose a new method to align obstacle maps without searching
explicit matches between features. The maps are obtained from a
stereo pair. Then, we use a vocabulary tree approach to identify
putative corresponding maps followed by a Newton minimization
algorithm to find the transformation that relates both maps. The
proposed method is evaluated on a typical office dataset showing
good performance.
Evaluation of the SIFT Object Recognition Method in Mobile Robots
Publication Type:
Conference PaperSource:
12th International Conference of the ACIA, IOS Press, Volume 202, Cardona, Spain, p.9-18 (2009)Keywords:
Computer Vision; Object Recognition; Mobile RobotsAbstract:
General object recognition in mobile robots is of primary importance
in order to enhance the representation of the environment that robots will use for
their reasoning processes. Therefore, we contribute reduce this gap by evaluating
the SIFT Object Recognition method in a challenging dataset, focusing on issues
relevant to mobile robotics. Resistance of the method to the robotics working conditions
was found, but it was limited mainly to well-textured objects.
Automatic Selection of Object Recognition Methods using Reinforcement Learning.
Object-based Place Recognition for Mobile Robots Using Panoramas
Publication Type:
Book ChapterSource:
Artificial Intelligence Research and Development (CCIA-2008), IOS Press, Volume 184, p.388-397 (2008)Keywords:
Object recognition; Robot localization; Mobile robots